Grenoble Workshop on Autonomic Computing and Control

Adaptive and reconfigurable computing systems are becoming widespread, in different areas such as Cloud computing, High-Performance Computing, or Smart Environments in the Internet of Things. Their particularity is to be able to change their computing structure dynamically, in response to changes in their environment or execution platform. Motivations for the adaptivity are found in resource management, energy efficiency, or functionality. Autonomic Computing in an approach to automate the management of adaptation with feedback loops, with a better handling of complexity and responsiveness. The design of the reconfiguration controllers requires methods, models and tools, in order to have safe and predictable behaviors of the automated systems, for which Control Theory provides an effective background.

This series of workshops provides opportunities for specialists of different aspects of the problem to meet and exchange approaches and experience, with presentations from different points of view on the topic, from software infrastructures to Control Theory.
This workshop follows three previous editions on october 7, 2015, may 27, 2014 and november 4, 2014, with copies of the slides, here https://persyval-lab.org/en/exploratory-project/staars

Fast values discovery embedded in big data emerges as the central operation for today’s information technology. Examples includes anomaly detection in large scale distributed systems, such as data center, mobile network, and power grid. Even with the powerful data processing engines, e.g., Spark and Storm, it is still no mean feat to ensure strong latency guarantee for real time big data discovery, yet in a resource efficient manner. To solve such a conundrum, approximate big data computing advocates to explicitly tradeoffs the analysis accuracy by reducing or altering data input via techniques like subsampling or lowering data precisions. In this talk, I will first introduce AccStream, a latency-aware approximate framework based on Spark Streaming. AccStream dynamically learns the latency model of analysis jobs via on-line probing technique and employs sampling theory to fulfill given accuracy targets. AccStream features on the design of hybrid windowing that trades off data freshness via a combination of tumbling and rolling windows, when encountering conflicts of latency and accuracy targets. In the second part of talk, I will present DApprox, a Differential Approximate framework that expands beyond AccStream and supports multi-priority workloads by using differentiated approximation ratios. Different from typical priority scheduling that evicts low-priority jobs, DApprox deflates their workload, abiding by the minimum accuracy requirement, thereby making resources available for high-priority jobs. I will preset both theoretical results and empirical validations on real systems.

This talk shortly reports on the results of the SALTY R&D project (https://salty.unice.fr) funded by the French funding agency (ANR). The key outcome of this project is a software framework that covers both design-time and runtime support for integrating self-adaptive behaviours into potentially complex legacy systems. SALTY therefore provides a reﬂective domain-speciﬁc model to externalise and make explicit the control layer of legacy systems. While the adoption of a domain-speciﬁc model leverages the mapping on diﬀerent middleware stacks (FraSCAti or Akka in our case studies), it also acts as a pivot, within a modular toolchain, to implement design-time veriﬁcations and to inject runtime guards. Future case studies of this approach will cover green computing, crowd- sensing, and big data systems.

The widespread use of mobile devices and location-based services has generated massive amounts of mobility databases. While processing these data is highly valuable (improvement of public transportation facilities, collaborative mapping, etc.), privacy issues can occur if personal information are released (frequently visited places, social relationships, etc.). Dedicated literature has investigated ways to protect mobility data by providing a large range of Location Privacy Protection Mechanisms. However, the privacy level of the protected data significantly varies depending on the protection mechanism used, its configuration and on the characteristics of the mobility data. Meanwhile, the protected data still needs to enable some useful post-processing. In order to tackle these issues, we present PULP, a framework that finds the suitable protection mechanism and configure it for each mobility database user in order to achieve user-defined objectives in terms of both privacy and utility.

16h00 –
Soguy Gueye (Inria Ctrl-A, Grenoble)

Autonomic Management of Missions and Reconfigurations in FPGA-based Embedded System

Implementing self-adaptive embedded systems, such as UV, involves an offline provisioning of the several implementations of the embedded functionalities with different characteristics in resource usage and performance in order for the system to dynamically adapt itself under uncertainties. FPGA-based architectures offer for support for high flexibility with dynamic reconfiguration features. We propose an autonomic control architecture for self-adaptive and self-reconfigurable FPGA-based embedded systems. The control architecture is structured in three layers: a mission manager, a reconfiguration manager and a scheduling manager. In this paper we focus on the design of the reconfiguration manager. We propose a design approach using automata-based discrete control. It involves reactive programming that provides formal semantics, and discrete controller synthesis from declarative objectives.

Autonomic Computing has recently contributed to the development of self-manageable Cloud services. It provides means to free Cloud administrators of the burden of manually managing varying-demand services while enforcing Service-Level Agreements (SLAs). All Cloud artifacts, regardless of the layer carrying them, share many common characteristics (e.g., resources, service-based model with consumers/providers). Thus, it should be possible to specify, (re)configure and monitor any XaaS (Anything-as-a-Service) layer in an homogeneous way.
To this end, the CoMe4ACloud approach proposes a generic model-based architecture for autonomic management of Cloud systems. We derive an unique and generic Autonomic Manager (AM) capable of managing any Cloud service, regardless of the layer. The generic AM is based on a generic constraint solver which tries to find the optimal configuration for the modeled XaaS and the best balance between costs and revenues while meeting constraints regarding the established SLA.

Recently, smart usage of renewable energy has been a hot topic in the Cloud community. Traditionally, data centers host heterogeneous applications, such as interactive and batch applications/jobs. Batch jobs arrives with deadline, hence can scheduled depending on the energy profile. On the contrary, interactive applications possess lesser flexibility, i.e., it should react with little to no latency, otherwise Quality of Service (QoS) can be seriously impacted. To cope up with these limitations, we have proposed a green energy adaptive solution to create green energy awareness inside the application that inherits the capability to smartly use the available green energy having static amount of underlying resources. This work adds to previous ones as it considers elastic underlying infrastructure, that is, we propose a PaaS solution which efficiently utilize the elasticity nature at both infrastructure and application levels, by leveraging adaptation in facing to changing condition i.e., workload burst, performance degradation, quality of energy, etc. While applications are adapted by dynamically re-configuring their service level based on performance and/or green energy availability, the infrastructure takes care of addition/removal of resources based on application’s resource demand. Both adaptive behaviors are implemented in separated modules and are coordinated in a sequential manner. We validate our approach by extensive experiments and results obtained over Grid’5000 test bed. Results show that, application can reduce significant amount of brown energy consumption by 35% and daily instance hour cost by 37% compared to a baseline approach when green energy aware adaptation is considered.

15h00 –
Issam Raïs (LIP/ENS, Inria Avalon, Lyon)

Shutdown policies with power capping for large scale computing systems

Large scale distributed systems are expected to consume huge
amounts of energy. To solve this issue, shutdown policies constitute an
appealing approach able to dynamically adapt the resource set to the
actual workload. However, multiple constraints have to be taken into
account for such policies to be applied on real infrastructures, in partic-
ular the time and energy cost of shutting down and waking up nodes,
and power capping to avoid disruption of the system. In this talk, I’ll present
models translating these various constraints into different shut-
down policies that can be combined. Our models are validated through
simulations on real workload traces and power measurements on real
testbeds.

Today’s control systems such as smart environments have the ability to adapt to their environment in order to achieve a set of objectives (e.g., comfort, security and energy savings). This is done by changing their behaviour upon the occurrence of specific events. Building such a system requires to design and implement autonomic loops that collect events and measurements, make decisions and execute the corresponding actions. The design and the implementation of such loops are made difficult by several factors: the complexity of systems with multiple objectives, the risk of conflicting decisions between multiple loops, the inconsistencies that can result from communication errors and hardware failures and the heterogeneity of the devices. In this paper, we propose a design framework for reliable and self-adaptive systems, where multiple autonomic loops can be composed into complex managers, and we consider its application to smart environments. We build upon the proposed framework a generic autonomic loop which combines an automata-based controller that makes correct and coherent decisions, a transactional execution mechanism that avoids inconsistencies, and an abstraction layer that hides the heterogeneity of the devices. We propose patterns for composition of such loops, in parallel, coordinated, and hierarchically, with benefits from the leveraging of automata-based modular constructs, that provides for guarantees on the correct behaviour of the controlled system. We implement our framework with the transactional middleware LINC, the reactive language Heptagon/BZR and the abstraction framework PUTUTU. A case study in the field of building automation is presented to illustrate the proposed framework.